Abstract

This work aims to define and experimentally evaluate an adaptive strategy based on neural learning to select an appropriate regularization parameter within a regularized restoration process. The appropriate setting of the regularization parameter within the restoration process is a difficult task attempting to achieve an optimal balance between removing edge ringing effects and suppressing additive noise. In this context,in an attempt to overcome the limitations of trial and error and curve fitting procedures we propose the construction of the regularization parameter function through a training concept using a Multilayer Perceptron neural network. The proposed solution is conceived independent from a specific restoration algorithm and can be included within a general local restoration procedure. The proposed algorithm was experimentally evaluated and compared using test images with different levels of degradation. Results obtained proven the generalization capability of the method that can be applied successfully on heterogeneous images never seen during training.